Research On The Optimal Allocation Of Power Resources In Full-duplex Communication System

In the era of Internet of things,the explosive growth of data flow,followed by the urgent demand for data.The existing communication system is faced with the problem of insufficient spectrum resources,so it is urgent to improve the utilization rate of spectrum resources.The emergence of full-duplex technology helps to solve the shortage of spectrum resource.In the non ideal environment,there are self-interference,co-channel interference and noise interference,which can not be completely eliminated due to the limitation of hardware equipment.Therefore,reasonable resource allocation management is the key to apply full-duplex technology to 5G communication.In addition,due to the lack of computing ability of mobile terminals,as well as the limitation of battery capacity,it is hindered to complete heavy computing tasks.Hence,in order to solve these problems effectively,the simultaneously wireless information and power transfer technology and mobile edge computing technology play an important role.Among them,the simultaneously wireless information and power transfer technology has solved the limitation of the battery capacity of mobile terminals.Mobile edge computing technology solves the problem of mobile terminal computing.In this paper,we first study joint user pairing and power allocation under non ideal channel state information.And combine user pairing and power allocation to maximize the system sum rate,and resist the impact of various interference on the system.Since the problem is a non convex optimization problem,we use an evolutionary algorithm based on decomposition and gradient projection to solve the non convex optimization problem.The simulation results show that under the condition of non ideal channel state information,the algorithm in this paper can effectively suppress the system performance degradation caused by interference.Among them,in the same CSI and RSI within 10 d B,compared with half-duplex system,the sum rate of the full-duplex MIMO cellular system has at least 20%improvement.In addition,we find that the channel estimation error has the greatest impact on the system sum rate through the study of the impact of different types of interference on the system performance.Secondly,in order to reduce the heavy computing burden of mobile terminals,make up for the lack of their computing power,and improve the spectrum utilization,we apply the full-duplex technology and mobile edge computing technology to the scenarios of the Internet of things,study the energy consumption of mobile terminals and the throughput of offloaded computing tasks.And then,we jointly optimize the CPU frequency,the transmission power of mobile terminals,and the uplink Transmission rate and offloaded computing tasks to minimize the uplink energy consumption and maximize offloaded computing task throughput.Since these two objective problems are non convex with high computational complexity,we use an algorithm of decomposition and iteration.First,the original problem is divided into two sub-problems.For the first one,we obtain the closed form solution by theoretical derivation.The closed form solution is substituted into sub-problem two,and the second two is further decomposed into several sub-problems.For these sub-problems,interior point algorithm is used to solve them respectively,and the final approximate solution is obtained through iteration.Simulation results show that the algorithm in this paper has lower uplink energy consumption and higher throughput of offloaded computing tasks compared with the comparison scheme.